Gradient \(\odot \)  Input [1]} is based on the gradient of a class score relative to the input, element-wise with the input, it was introduced to improve the sharpness of the attribution maps. A theoretical analysis conducted by [2]} showed that Gradient \(\odot \)  Input is equivalent to \(\epsilon \) -LRP and DeepLIFT [1]} methods under certain conditions – using a baseline of zero, and with all biases to zero.
\( {g}(x) = x \odot ||\nabla _{x} {f}(x)|| \) 
